Programming Tech Lab

When your dataset is small, your problems are usually big. I still remember the first time I trained a machine learning model on a dataset with fewer than 1,000 rows. I followed all the “best practices” — cross-validation, feature scaling, hyperparameter tuning — and yet the results were disappointing. If you’ve worked with real-world data, this probably sounds familiar. Most datasets are not massive. They’re messy, limited, and expensive to collect. That’s where TabPFN comes in — a powerful approach designed specifically for small tabular datasets . The Problem with Small Datasets Most machine learning tutorials assume you have: Tens of thousands of samples Enough data for train, validation, and test splits Room for trial and error In reality, we often deal with: 300 medical records 800 customer profiles 500 survey responses With small datasets, models overfit easily, tuning becomes unstable, and deep learning usually fails. TabPFN was built ...